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. 2010 Winter;40(1):173-194.
doi: 10.1177/002204261004000110.

Influences of a Covariate on Growth Mixture Modeling

David Huang  1 , Mary-Lynn Brecht, Motoaki Hara, Yih-Ing Hser
Affiliations

Influences of a Covariate on Growth Mixture Modeling

David Huang et al. J Drug Issues. 2010 Winter.

Abstract

This study investigated the influence of including a covariate and/or a distal outcome on growth mixture modeling (GMM). GMM was used to examine patterns of days of heroin use over 16 years among 471 heroin users and the relationship of those patterns to mortality (distal outcome). Comparisons were made among four types of models: without a covariate and a distal outcome (two-stage approach), with a distal outcome, with a covariate, and with a covariate and a distal outcome in conjunction with three different covariates. The two-stage approach and models with the inclusion of a distal outcome resulted in different conclusions when testing the impact of latent trajectory membership on the distal outcome. Differences in membership classifications between unconditional and conditional models were mainly determined by two factors: (1) the associations of the trajectories with the covariate and the distal outcome, and (2) the distribution of the covariate in the study sample.

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Figures

Figure 1
Figure 1
The Four Sets of Growth Mixture Models Examined in the Study
Figure 2
Figure 2. The Four Heroin-use Trajectories Identified by the Unconditional Model (Model 1)
See this image and copyright information in PMC

References

    1. Dekker MC, Ferdinand RF, Van Lang NDJ, Bongers IL, Van Der Ende J, Verhulst FC. Developmental trajectories of depressive symptoms from early childhood to late adolescence: Gender differences and adult outcome. Journal of Child Psychology and Psychiatry. 2007;48(7):657–666. - PubMed
    1. D’Unger AV, Land KC, McCall PL. Sex differences in age patterns of delinquent/criminal careers: Results from Poisson latent class analyses of the Philadelphia cohort study. Journal of Quantitative Criminology. 2002;18(4):349–375.
    1. D’Unger AV, Land KC, McCall PL, Nagin DS. How many latent classes of delinquent/criminal? Results from mixed Poisson regression analyses of the London, Philadelphia, and Racine cohorts studies. American Journal of Sociology. 1998;103:1593–1630.
    1. Eggleston EP, Laub JH, Sampson RJ. Methodological sensitivities to latent class analysis of long-term criminal trajectories. Journal of Quantitative Criminology. 2004;20(1):1–25.
    1. Feng X, Shaw DS, Silk JS. Developmental trajectories of anxiety symptoms among boys across early and middle childhood. Journal of Abnormal Psychology. 2008;117(1):32–47. - PMC - PubMed

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